Using N/DPAs to Achieve Global Settlement: Lessons for Canada and its Nascent Regime
Bibliographic record
Abstract
Deferred prosecution agreements (DPAs) and non-prosecution agreements (NPAs) have become increasingly common non-trial settlement tools to resolve corporate crimes domestically. Due to the complexity of investigating misconduct by corporations that operate transnationally, significant challenges remain in coordinating settlements across jurisdictions. This paper aims to show how multi-jurisdictional corporate cases are typically handled and explores the possible advantages of using N/DPAs to achieve global settlement as an alternative to prosecution. Canada has recently enacted legislation enabling the use of DPAs in the resolution of certain corporate criminal disputes. The creation of the Canadian Remediation Agreement Regime comes at a unique time, as jurisdictions are beginning to collaborate to resolve multi-jurisdictional corporate crimes through N/DPAs. As one of the newest non-trial settlement frameworks, it is possible for Canada to create a remediation agreement regime that favours multi-jurisdictional coordination and global settlement from the outset. This paper considers the possibility of achieving global settlements through N/DPAs and makes recommendations for the Canadian regime with a view to fostering increased collaboration between enforcement agencies on the international stage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".